An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

Abstract

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

Machine learning applications in the diagnosis of leukemia: Current trends and future directions

“…In addition, handsearching of relevant studies and the top Google Scholar results yielded 40 studies for full‐text review. After removing duplicates, twenty‐three (23) studies satisfied the inclusion criteria (see Figure ). The studies were classified according to the type of leukemia into: ALL (13), AML (8), CLL (3), and CML (1).…”

“…Compared to other leukemia subsets, pathology diagnosis of ALL was the subset with the higher number of studies. Of the included studies, 13 studies investigated the role of ML tools in ALL diagnosis, with 12 studies applied ML tools on microscopic diagnosis and one study applied them on flow cytometric diagnosis . Seven of the included studies, applying ML on microscopic diagnosis, used only peripheral blood smears, with four studies using bone marrow slides along with blood smears.…”

“…Eight studies have utilized cross‐validation as a validation technique for their models. Chin Neoh et al used bootstrap sampling in addition to cross‐validation. On the other hand, Shafique & Tehsin et al used new set of images to serve as a validation technique.…”

Feature Extraction Techniques for Leukocyte Classification - A Review

“…The segmentation is used to segment nucleus and cytoplasm from leukocytes [11].The feature extraction is the next step after segmentation. Fig.3 shows the examples of subtypes of Leukocytes.…”

Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception

“…Most of these methods utilize conventional image processing and machine learning techniques, which involve mainly segmentation, feature extraction, and classification methods. Especially the segmentation and feature extraction phases are considered the most significant and challenging tasks ( Neoh et al, 2015 ). The main reason lies in the large variety of blood smear images, taken under different conditions, and the potential morphological differences between blast cells.…”